Accurate mapping of offshore aquaculture remains challenging in complex coastal environments due to heterogeneous backgrounds, variable sea states, blurred pond boundaries, adhesion among densely distributed cages, and the weak texture of floating rafts. To address these limitations, this study proposes a bi-temporal multimodal cross-fusion network (BMCF-Net) for fine-scale offshore aquaculture segmentation from Sentinel-1/2 imagery. The framework jointly exploits bi-temporal observations acquired during non-ice and sea-ice periods and integrates them through a bi-temporal fusion module to enhance target–background separability and suppress environmental noise. In addition, a feature correction module and a multi-head feature fusion module are introduced to strengthen cross-modal alignment between SAR structural information and optical spectral–textural cues, thereby improving the separation of dense aquaculture units and the detection of weak-texture targets. Experiments conducted on a multimodal dataset from the Liaoning coastal zone show that BMCF-Net achieves F1-scores of 93.15%, 93.90%, and 89.04% for aquaculture ponds, cages, and floating rafts, respectively, outperforming state-of-the-art segmentation models such as FTransUNet. The proposed model was further applied to produce a high-resolution aquaculture distribution map for Liaoning Province in 2023 and to analyze area dynamics over the past six years. The results demonstrate the potential of BMCF-Net for large-scale offshore aquaculture monitoring and coastal resource management.
Liang et al. (Mon,) studied this question.